Medical image reconstruction requires high performance computing (HPC) and high-end computing resources. They are extemely critical for practical implementation of cutting-edge technology in CT/micro-CT medical imaging. Although the recently developed algorithms are very sophisticated, they require significant time for 3-D image reconstruction. It has been a challenge for decades to find an economic and efficient parallel algorithm, and high performance computing system. The recently developed parallel Katsevich algorithm for 3D cone-beam CT image reconstruction at the University of Iowa has prompted the investigators to develop a large-scale parallel Katsevich algorithm. This algorithm will be developed and implemented for high-resolution CT/micro-CT medical image reconstructions using NSF TeraGrid system which integrates a massive number of processors. The overall goal of this proposal is to develop a specific parallel algorithm for 3-D CT/micro-CT medical image reconstruction on large scale heterogeneous systems. This parallel algorithm will allow medical researchers and/or clinical professionals to achieve high-performance for high-resolution, 3-D medical image reconstruction on a large-scale distributed computing system integrating multiple HPC clusters. The specific aims of this R21 project are to (1) develop large-scale parallel Katsevich algorithm on high performance computing systems with focuses on memory allocation, projection data decomposition, and scalability; (2) develop functions which can account for load balancing, fault-tolerance, and network impact in distributed environment; (3) compute benchmarks for evaluation of parallel performance in terms of speed-up, parallel efficiency, scalability, granularity, and network latency, using TeraGrid supercomputing resources. [unreadable] [unreadable] [unreadable]